SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice

• URL: http://arxiv.org/abs/2206.14855v2
• Date: Thu, 27 Oct 2022 23:26:05 GMT
• Title: SoK: Content Moderation in Social Media, from Guidelines to Enforcement, and Research to Practice
• Authors: Mohit Singhal and Chen Ling and Pujan Paudel and Poojitha Thota and Nihal Kumarswamy and Gianluca Stringhini and Shirin Nilizadeh
• Abstract summary: We study the 14 most popular social media content moderation guidelines and practices in the US. We identify the differences between the content moderation employed in mainstream social media platforms compared to fringe platforms. We highlight why platforms should shift from a one-size-fits-all model to a more inclusive model.
• Score: 9.356143195807064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To counter online abuse and misinformation, social media platforms have been establishing content moderation guidelines and employing various moderation policies. The goal of this paper is to study these community guidelines and moderation practices, as well as the relevant research publications to identify the research gaps, differences in moderation techniques, and challenges that should be tackled by the social media platforms and the research community at large. In this regard, we study and analyze in the US jurisdiction the fourteen most popular social media content moderation guidelines and practices, and consolidate them. We then introduce three taxonomies drawn from this analysis as well as covering over one hundred interdisciplinary research papers about moderation strategies. We identified the differences between the content moderation employed in mainstream social media platforms compared to fringe platforms. We also highlight the implications of Section 230, the need for transparency and opacity in content moderation, why platforms should shift from a one-size-fits-all model to a more inclusive model, and lastly, we highlight why there is a need for a collaborative human-AI system.

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